CN112820379A - Intelligent diet recommendation method and system integrating user images - Google Patents

Intelligent diet recommendation method and system integrating user images Download PDF

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CN112820379A
CN112820379A CN202110105434.8A CN202110105434A CN112820379A CN 112820379 A CN112820379 A CN 112820379A CN 202110105434 A CN202110105434 A CN 202110105434A CN 112820379 A CN112820379 A CN 112820379A
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dish
user
dishes
list
data
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CN112820379B (en
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李宗博
陈伯怀
杜小军
杜乐
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Wuzheng Intelligent Technology Beijing Co ltd
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    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof

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Abstract

The invention discloses an intelligent diet recommendation method and system integrating user images, which are characterized by comprising the following steps: acquiring daily dish data, preprocessing the daily dish data, and constructing a dish library; standardizing dish data in a dish library, and calculating nutrient components contained in the dishes according to a food material nutrient component content standard table; constructing a user portrait based on the attribute information of the user and the historical eating behavior of the user; and recommending the dishes based on the user portrait and the nutritional ingredients contained in the dishes. According to the intelligent diet recommendation method and system, intelligent diet recommendation is performed by fusing the user images, positive health guidance and early warning of related diseases can be performed on recent diet behaviors of the user, and health management is facilitated.

Description

Intelligent diet recommendation method and system integrating user images
Technical Field
The invention belongs to the technical field of health management, and particularly relates to an intelligent diet recommendation method and system integrating user images.
Background
With the rapid development of social economy in China, the living standard and medical and sanitary conditions of people are greatly improved. Modern medical research shows that daily reasonable diet and balanced food nutrition can promote body health and prevent diseases.
The unbalanced nutrition in the diet not only can cause the disease risk caused by the deficiency of the essential nutrient elements, such as chronic diseases of diabetes, obesity, hypertension, cancer and the like. In addition, chronic diseases become the main cause of death of residents in China, and the importance of diet health problems has attracted extensive attention of people. What is eaten every day, how much is eaten, whether nutrition is unbalanced, whether potential chronic disease risk exists and other health diet problems become problems that people need to answer urgently.
Disclosure of Invention
In view of the above, the invention provides an intelligent diet recommendation method and system integrating user images, which are used for solving the problems that diet management of a user is not fine enough and potential risk prediction cannot be carried out.
The invention discloses an intelligent diet recommendation method integrating user images in a first aspect, which comprises the following steps:
acquiring daily dish data, preprocessing the daily dish data, and constructing a dish library;
standardizing dish data in a dish library, and calculating nutrient components contained in the dishes according to a food material nutrient component content standard table;
the user portrait is constructed based on the attribute information of the user and the historical eating behavior of the user;
and recommending the dishes based on the user portrait and the nutritional ingredients contained in the dishes.
Preferably, the dish data mainly comprises dish names, dish categories, efficacies, tastes, dish pictures, main material information, auxiliary material information, seasoning information, cooking time and cooking modes.
Preferably, the pretreatment comprises:
carrying out format conversion on data types of dish data, wherein the data types comprise numerical types, text types and picture types; for text-type data, the text in each field that relates to a quantifier is converted to numerical-type data.
Preferably, the attribute information of the user comprises 8 dimensions of the age, the sex, the height, the weight, the allergy history, the crowd category, the physique category and the existing medical history; the group categories comprise the elderly, middle aged people, young people, middle school students, primary school students, infants, children, pregnancy preparation period, early pregnancy period, middle pregnancy period, late pregnancy period, month period and climacteric period; the constitutional types include yang preponderance, phlegm dampness, damp-heat, yin deficiency, yang deficiency, qi deficiency, specific endowment, blood stasis and qi stagnation.
Preferably, the constructing a user portrait based on the user attribute information, the user historical eating behavior and the nutritional ingredients contained in the dishes, and the recommending the dishes based on the user portrait specifically comprises:
filtering unsuitable or even contraindicated dishes based on the attribute information of the user to generate a first dish list;
based on the historical eating behaviors of the user, historical preference dishes are calculated, and similar dishes are screened to generate a second dish list;
and screening the dishes which appear in the first dish list and the second dish list at the same time to form an optimal dish recommendation list, and recommending the optimal dish recommendation list to the user.
Preferably, the filtering of unsuitable or even contraindicated dishes based on the attribute information of the user itself includes:
a. generating a standard dish recommendation list-1 from a dish library according to the age, the sex, the height and the weight of a user;
b. performing preliminary filtering on the dish suitable for eating on the standard dish recommendation list-1 according to the crowd category characteristics, the constitution category characteristics and the past medical history characteristics of the user respectively, and obtaining a dish list-2 of a reasonable nutrition intake interval by combining the nutritional ingredients contained in the dish;
c. counting nutrient elements which do not reach the standard and exceed the standard of the food in the period according to the historical eating behaviors of the user, screening a food list-2 by taking the nutrient elements as filtering conditions, and filtering the food which exceeds the fat content standard by combining with the body mass index BIM of the user to obtain a new food list-3;
d. and (3) screening dishes containing food materials related to the user allergy history in the dish list-3 according to the main materials, auxiliary materials and seasoning components in the dishes, finally generating a dish recommendation list-4, and taking the recommendation list-4 as a first dish list.
Preferably, the calculating historical preference dishes of the user based on the historical eating behaviors of the user, and the screening of similar dishes to perform second dish list recommendation specifically includes:
adopting one-hot code to express the dish object;
performing cluster division on dish objects in the historical eating behaviors through a K-means clustering algorithm to obtain the number of dishes in each cluster and the corresponding dish category;
screening out the clustering clusters containing the largest number of dishes, and taking the dishes in the screened clustering clusters as the dishes historically preferred by the user;
and calculating the similarity between the dishes in the screened cluster and the dishes in the dish library, and screening out the related dishes with the similarity higher than a preset threshold value as a second dish recommendation list.
Preferably, the method further comprises: the method comprises the steps of monitoring historical dietary data information of a user in real time, carrying out correlation prediction on ingested food nutrition and common diseases by combining with attribute information of the user, outputting potential risks which may appear in a recent period of time of dietary behaviors, and generating a report of health and disease risks of the user.
In a second aspect of the present invention, an intelligent diet recommendation system integrated with user images is disclosed, the system comprising:
a dish library construction module: the system is used for acquiring daily dish data and preprocessing the daily dish data to construct a dish library;
a data preprocessing module: the food material nutrient content standard table is used for standardizing the dish data in the dish library and calculating the nutrient content of the dish through the food material nutrient content standard table;
the dish recommending module: the system is used for constructing a user portrait based on user attribute information and user historical dietary behaviors and recommending dishes based on the user portrait and nutritional ingredients contained in the dishes; the dish recommending unit specifically comprises:
a first menu list unit: the method comprises the steps of filtering unsuitable or even contraindicated dishes based on attribute information of a user to generate a first dish list;
a second dish list unit: the dish selection system is used for calculating historical preference dishes based on historical eating behaviors of the user, and screening similar dishes to generate a second dish list;
an optimal dish list unit: the method comprises the steps of screening out dishes which appear in the first dish list and the second dish list at the same time to form an optimal dish recommendation list, and recommending the optimal dish recommendation list to a user;
an analysis early warning module: the method is used for monitoring historical dietary data information of the user in real time, performing correlation prediction on ingested food nutrition and common diseases, outputting potential risks which may occur in recent eating behaviors for a period of time, and generating a report of health and disease risks of the user.
Compared with the prior art, the invention has the following beneficial effects:
1) according to the method, a user portrait is constructed based on user self attribute information and user historical diet behaviors, inappropriate or even contraindicated dishes are filtered based on the user self attribute information, historical preference dishes are calculated based on the user historical diet behaviors, and the historical preference dishes and the historical diet behaviors are intersected to form an optimal dish recommendation list to be recommended to a user, so that the problem that dish fluctuation is large due to random recommendation is avoided, and a dish list which is suitable for the user diet preference and meets the nutrition requirements of the tiger is recommended finally;
2) the dish recommendation method based on the user portrait and nutritional ingredients contained in the dishes can be used for carrying out active health guidance on recent eating behaviors of the user, meanwhile, historical diet data information of the user can be monitored in real time, relevant prediction on ingested dish nutrition and common diseases is carried out by combining attribute information of the user, early warning information of relevant diseases possibly generated by diet health states of the user and ingested diet is monitored in real time, and active help is provided for the user in the aspect of intelligent healthy diet to the maximum extent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an intelligent diet recommendation method with user image fusion according to the present invention;
fig. 2 is a schematic structural diagram of the intelligent diet recommendation system integrating user images.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, the present invention provides an intelligent diet recommendation method with user images fused, including:
s1, acquiring daily dish data and preprocessing the daily dish data to construct a local dish library;
specifically, the dish data mainly comprises dish names, dish categories, efficacies, tastes, dish pictures, main material information, auxiliary material information, seasoning information, cooking time and cooking modes. The fields such as dish names, main materials, auxiliary materials and seasonings play an important role in diet analysis of users, so the fields are used as core important fields for subsequent processing, and other fields are used for interface display.
Carrying out format conversion on data types of dish data, wherein the data types comprise numerical types, text types and picture types; for text-type data, the text in each field that relates to a quantifier is converted to numerical-type data. In order to make the dish data easy to standardize, standardize and specially convert the information such as 'root', 'ml', 'box', 'jin', 'only', 'block', 'granule', 'two', 'piece', 'bottle', 'spoon', 'bar', 'head', 'bowl', 'little handle', 'small cup', 'small scoop', 'small butterfly', 'small segment', 'small block', 'small scoop' and 'small bowl' in the fields of the dish name, main material, auxiliary material and seasoning into proper numerical data, necessary numerical conversion processing is carried out for the subsequent numerical calculation and the like in the user portrait model.
S2, standardizing dish data in a dish library, and calculating the nutrient content of the dish through a food material nutrient content standard table;
as the main material, the auxiliary material, the seasoning and other information fields in the dish contain rich nutritional ingredient information, the analysis and estimation of whether the dish has a nutritional imbalance risk for a user has important significance, and the total amount of each nutritional ingredient of the dish is calculated by adopting a food material nutritional ingredient content standard table (the content of each hundred grams or milliliters or milligrams). Wherein the nutrient components comprise carbohydrate, fat, protein, cellulose, vitamin A, vitamin C, vitamin E, carotene, thiamine, riboflavin, nicotinic acid, cholesterol, magnesium, calcium, iron, zinc, copper, manganese, potassium, phosphorus, sodium, selenium, etc. The non-trace elements are calculated per hundred grams, and the trace elements are calculated per hundred milligrams. It can be known that the difference of the values of the contents of trace elements and non-trace elements in the nutrient elements is large, and the trace elements occasionally have blank values and other problems, so the method is very important for the standardized processing of dish data. The nutrient content missing value of the dish is uniformly filled to be 0, all nutrient data are standardized by max-min, and the influence of different dimensional data on numerical calculation is eliminated.
S3, constructing a user portrait based on the attribute information of the user and the historical eating behavior of the user; and recommending the dishes based on the user portrait and the nutritional ingredients contained in the dishes.
The personal attribute information of the user comprises 8 dimensions of the age, the sex, the height, the weight, the allergy history, the crowd type, the constitution type and the existing medical history; the group categories comprise the elderly, middle aged people, young people, middle school students, primary school students, infants, children, pregnancy preparation period, early pregnancy period, middle pregnancy period, late pregnancy period, month period and climacteric period; the constitution types include yang preponderance, phlegm dampness, damp-heat, yin deficiency, yang deficiency, qi deficiency, specific endowment, blood stasis, qi stagnation and the like; the past disease information of a user is related to common diseases such as enteritis, cholelithiasis, traumatic fracture injury, arteriosclerosis, tinnitus, cancer prevention and resistance, emphysema, hepatitis, cirrhosis, hypertension, hyperlipidemia, menopause, osteoporosis, arthritis, coronary heart disease, thyroid, tuberculosis, oral ulcer, measles, lithangiuria, anemia, prostate, nephritis, postoperative diseases such as diabetes, gout, dysmenorrheal, gastritis, peptic ulcer, infantile enuresis, pharyngitis, malnutrition, irregular menstruation, bronchitis, fatty liver, hemorrhoids, stroke, uterine prolapse and the like.
The historical diet behavior of the user is a diet record of the user in a recent period of time, and the user portrait can be constructed based on the attribute information of the user and the historical diet behavior.
The dish recommendation method based on the user portrait and the nutritional ingredients contained in the dish specifically comprises the following steps:
s31, filtering unsuitable or even contraindicated dishes based on the attribute information of the user to generate a first dish list, which specifically comprises the following steps:
a. generating a standard dish recommendation list-1 from a dish library according to the age, the sex, the height and the weight of a user;
b. performing preliminary filtering on the dish suitable for eating on the standard dish recommendation list-1 according to the crowd category characteristics, the constitution category characteristics and the past medical history characteristics of the user respectively, and obtaining a dish list-2 of a reasonable nutrition intake interval by combining the nutritional ingredients contained in the dish;
c. counting nutrient elements which do not reach the standard and exceed the standard of the food in the period according to the historical eating behaviors of the user, screening a food list-2 by taking the nutrient elements as filtering conditions, and filtering the food which exceeds the fat content standard by combining with the body mass index BIM of the user to obtain a new food list-3;
d. and (3) screening dishes containing food materials related to the user allergy history in the dish list-3 according to the main materials, auxiliary materials and seasoning components in the dishes, finally generating a dish recommendation list-4, and taking the recommendation list-4 as a first dish list.
S32, calculating user preference dishes based on historical eating behaviors of the user, screening similar dishes to generate a second dish list, and specifically comprising the following steps:
a', adopting one-hot coding to express dish objects;
b', performing cluster division on the dish objects in the historical eating behaviors through a K-means clustering algorithm to obtain the number of dishes in each cluster and the corresponding dish category; in particular, given a cluster K and a user historical diet dataset T ═ T1,t2,…,tn},ti＝(xi,yi) And taking the distance between the dish data objects as a clustering standard to perform K-means clustering.
c', screening out the clustering clusters containing the largest number of dishes, and searching for the dishes historically preferred by the user from the screened clustering clusters;
d', calculating the similarity between the dish historically preferred by the user and the dish in the dish library, and screening out the dish with the similarity higher than a preset threshold value to serve as a second dish recommendation list.
Since the history time is short, K can be selected as 5, 5 clustering center points are found, and 5 dish clusters of the user are obtained through a clustering algorithm. And calculating and searching the most preferred dishes of the user from the cluster with the most dishes, wherein the dish closest to the cluster center point is adopted as the most preferred dish of the user. And finally, calculating cosine similarity according to the most preferred dishes and the dish data set processed by the data preprocessing unit, sorting in a descending order, and finally recommending by taking the first N dishes to form a list, wherein the value of N is 15. According to the method, the user portrait of the historical diet behavior information of the user is taken as a basis, and the recent diet preference of the user is mined, so that the problem that dish fluctuation is large due to random recommendation is avoided, and a dish list suitable for the diet preference of the user is recommended finally.
S33, screening out the dishes which appear in the first dish list and the second dish list at the same time to form an optimal dish recommendation list, and recommending the optimal dish recommendation list to the user.
S4, monitoring historical dietary data information of the user in real time, performing correlation prediction on ingested food nutrition and common diseases by combining attribute information of the user, outputting potential risks which may occur in a recent period of time of dietary behaviors, and generating a report of health and disease risks of the user.
Specifically, determining the range of the diet dishes corresponding to the user according to the attribute information of the user; determining a preference diet dish range corresponding to the user according to recent historical diet behavior data of the user; based on common malnutrition and other chronic disease common knowledge, the early warning analysis unit uses a statistical analysis module to respectively express recent nutrient elements in global or local expression forms according to statistical concepts such as sum, mean value, variance and the like; and (3) performing association prediction on the nutrient elements and common diseases by using a disease association module, outputting potential risks which may appear in eating behaviors in a recent period of time, and finally printing information generated by the two modules as a user health and disease risk report.
The intelligent diet recommendation method integrating the user picture can actively guide the recent diet behavior of the user, can monitor the diet health state of the user in real time and output early warning information of related diseases possibly generated by diet, and provides positive help for the user in the aspect of intelligent healthy diet to the maximum extent.
Referring to fig. 2, corresponding to the above method embodiment, the present invention further provides an intelligent diet recommendation system integrating user images, the system includes:
the dish library construction module 10: the system is used for acquiring daily dish data and preprocessing the daily dish data to construct a dish library;
the data preprocessing module 20: the food material nutrient content standard table is used for standardizing the dish data in the dish library and calculating the nutrient content of the dish through the food material nutrient content standard table;
the dish recommendation module 30: the system is used for constructing a user portrait based on the attribute information of the user and nutritional ingredients contained in dishes in the historical eating behaviors of the user, and recommending the dishes based on the user portrait; the dish recommending unit specifically comprises:
a first menu list unit: the method comprises the steps of filtering unsuitable or even contraindicated dishes based on attribute information of a user to generate a first dish list;
a second dish list unit: the dish selection system is used for calculating historical preference dishes based on historical eating behaviors of the user, and screening similar dishes to generate a second dish list;
an optimal dish list unit: the method comprises the steps of screening out dishes which appear in the first dish list and the second dish list at the same time to form an optimal dish recommendation list, and recommending the optimal dish recommendation list to a user;
the analysis early warning module 40: the method is used for monitoring historical dietary data information of the user in real time, performing correlation prediction on ingested food nutrition and common diseases, outputting potential risks which may occur in recent eating behaviors for a period of time, and generating a report of health and disease risks of the user.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (9)

1. An intelligent diet recommendation method integrating user images is characterized by comprising the following steps:
acquiring daily dish data, preprocessing the daily dish data, and constructing a dish library;
standardizing dish data in a dish library, and calculating nutrient components contained in the dishes according to a food material nutrient component content standard table;
constructing a user portrait based on the attribute information of the user and the historical eating behavior of the user;
and recommending the dishes based on the user portrait and the nutritional ingredients contained in the dishes.
2. The intelligent food recommendation method integrating user portrayal as claimed in claim 1, wherein the dish data mainly comprises dish names, dish categories, efficacies, tastes, dish pictures, main material information, auxiliary material information, seasoning information, cooking time and cooking modes.
3. The intelligent diet recommendation method integrating user images as claimed in claim 1, wherein the preprocessing comprises:
carrying out format conversion on data types of dish data, wherein the data types comprise numerical types, text types and picture types; for text-type data, the text in each field that relates to a quantifier is converted to numerical-type data.
4. The intelligent diet recommendation method integrating user images as claimed in claim 1, wherein the user's own attribute information comprises 8 dimensions of the age, gender, height, weight, allergy history, crowd category, physique category and past medical history; the group categories comprise the elderly, middle aged people, young people, middle school students, primary school students, infants, children, pregnancy preparation period, early pregnancy period, middle pregnancy period, late pregnancy period, month period and climacteric period; the constitutional types include yang preponderance, phlegm dampness, damp-heat, yin deficiency, yang deficiency, qi deficiency, specific endowment, blood stasis and qi stagnation.
5. The intelligent diet recommendation method integrating the user image according to claim 4, wherein the dish recommendation based on the user image and the nutrient content of the dish specifically comprises the following steps:
filtering unsuitable or even contraindicated dishes based on the attribute information of the user to generate a first dish list;
based on the historical eating behaviors of the user, historical preference dishes are calculated, and similar dishes are screened to generate a second dish list;
and screening the dishes which appear in the first dish list and the second dish list at the same time to form an optimal dish recommendation list, and recommending the optimal dish recommendation list to the user.
6. The intelligent diet recommendation method integrating the user images as claimed in claim 5, wherein the filtering of unsuitable or even contraindicated dishes based on the user's own attribute information specifically comprises:
a. generating a standard dish recommendation list-1 from a dish library according to the age, the sex, the height and the weight of a user;
b. performing preliminary filtering on the dish suitable for eating on the standard dish recommendation list-1 according to the crowd category characteristics, the constitution category characteristics and the past medical history characteristics of the user respectively, and obtaining a dish list-2 of a reasonable nutrition intake interval by combining the nutritional ingredients contained in the dish;
c. counting nutrient elements which do not reach the standard and exceed the standard of the food in the period according to the historical eating behaviors of the user, screening a food list-2 by taking the nutrient elements as filtering conditions, and filtering the food which exceeds the fat content standard by combining with the body mass index BIM of the user to obtain a new food list-3;
d. and (3) screening dishes containing food materials related to the user allergy history in the dish list-3 according to the main materials, auxiliary materials and seasoning components in the dishes, finally generating a dish recommendation list-4, and taking the recommendation list-4 as a first dish list.
7. The intelligent diet recommendation method integrating the user images as claimed in claim 5, wherein the calculating of the historical preference dishes of the user based on the historical diet behavior of the user and the screening of similar dishes for the second dish list recommendation specifically comprises:
adopting one-hot code to express the dish object;
performing cluster division on dish objects in the historical eating behaviors through a K-means clustering algorithm to obtain the number of dishes in each cluster and the corresponding dish category;
screening out the clustering clusters containing the largest number of dishes, and searching for dishes historically preferred by the user from the screened clustering clusters;
and calculating the similarity between the historical preference dishes of the user and the dishes in the dish library, and screening out the related dishes with the similarity higher than a preset threshold value as a second dish recommendation list.
8. The intelligent diet recommendation method integrating user images as claimed in claim 1, wherein the method further comprises:
the method comprises the steps of monitoring historical dietary data information of a user in real time, carrying out correlation prediction on ingested food nutrition and common diseases by combining with attribute information of the user, outputting potential risks which may appear in a recent period of time of dietary behaviors, and generating a report of health and disease risks of the user.
9. An intelligent diet recommendation system fusing user portraits, the system comprising:
a dish library construction module: the system is used for acquiring daily dish data and preprocessing the daily dish data to construct a dish library;
a data preprocessing module: the food material nutrient content standard table is used for standardizing the dish data in the dish library and calculating the nutrient content of the dish through the food material nutrient content standard table;
the dish recommending module: the system is used for constructing a user portrait based on user attribute information and user historical dietary behaviors and recommending dishes based on the user portrait and nutritional ingredients contained in the dishes; the dish recommending unit specifically comprises:
a first menu list unit: the method comprises the steps of filtering unsuitable or even contraindicated dishes based on attribute information of a user to generate a first dish list;
a second dish list unit: the dish selection method comprises the steps of calculating historical preference dishes of a user based on historical eating behaviors of the user, and screening similar dishes to generate a second dish list;
an optimal dish list unit: the method comprises the steps of screening out dishes which appear in the first dish list and the second dish list at the same time to form an optimal dish recommendation list, and recommending the optimal dish recommendation list to a user;
an analysis early warning module: the method is used for monitoring the historical dietary data information of the user in real time, performing correlation prediction on the nutrition of the ingested dishes and common diseases by combining the attribute information of the user, outputting potential risks which may appear in the dietary behaviors in a short period of time, and generating a report of the health and disease risks of the user.
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Cited By (3)

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